#这个文件是直接抄tensorflow官方样例的，可以直接运行
#这是基于CNN的手写字体识别，就是两个卷积层加全连接层
#原文地址 http://www.tensorfly.cn/tfdoc/tutorials/mnist_pros.html


from tensorflow.examples.tutorials.mnist import input_data
print ("loading data...")
mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)
print ("data loaded.")

import tensorflow as tf

sess=tf.InteractiveSession()
x=tf.placeholder("float",[None,28*28])
y_=tf.placeholder("float",[None,10])

def weight_variable(shape):
    initial=tf.truncated_normal(shape,stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial=tf.constant(0.1,shape=shape)
    return tf.Variable(initial)

def conv2d(x,w):
    return tf.nn.conv2d(x,w,strides=[1,1,1,1,],padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')


w_conv1=weight_variable([5,5,1,32])
b_conv1=bias_variable([32])

x_image=tf.reshape(x,[-1,28,28,1]) 

h_conv1=tf.nn.relu(conv2d(x_image,w_conv1)+b_conv1)
h_pool1=max_pool_2x2(h_conv1)

w_conv2=weight_variable([5,5,32,64])
b_conv2=bias_variable([64])

h_conv2=tf.nn.relu(conv2d(h_pool1,w_conv2)+b_conv2)
h_pool2=max_pool_2x2(h_conv2)

w_fc1=weight_variable([7*7*64,1024])
b_fc1=bias_variable([1024])

h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*64])
h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,w_fc1)+b_fc1)

keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
sess.run(tf.initialize_all_variables())
for i in range(20000):
  batch = mnist.train.next_batch(50)
  if i%100 == 0:
    train_accuracy = accuracy.eval(feed_dict={
        x:batch[0], y_: batch[1], keep_prob: 1.0})
    print ("step %d, training accuracy %g"%(i, train_accuracy))
  train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

print ("test accuracy %g"%accuracy.eval(feed_dict={
    x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))